[MNT] basic version of learnware market workflow

docs/introduction/quick.rst

@@ -109,19 +109,46 @@ Users can start an Learnware Market workflow according to the following steps:
         "Task": {
             "Values": ["Classification"],
             "Type": "Class",
-        },
-        "Device": {"Values": ["GPU"], "Type": "Tag"},
-        "Scenario": {"Values": ["Business"], "Type": "Tag"},
-        "Description": {"Values": "", "Type": "String"},
-        "Name": {"Values": "", "Type": "String"},
-    }
-    user_info = BaseUserInfo(id="user_0", semantic_spec=user_semantic)
-    _, single_learnware_list, _ = easy_market.search_learnware(user_info)
+            },
+            "Device": {"Values": ["GPU"], "Type": "Tag"},
+            "Scenario": {"Values": ["Business"], "Type": "Tag"},
+            "Description": {"Values": "", "Type": "String"},
+            "Name": {"Values": "", "Type": "String"},
+        }
+        user_info = BaseUserInfo(id="user", semantic_spec=user_semantic)
+        _, single_learnware_list, _ = easy_market.search_learnware(user_info)
 
 4. Statistical specification search:
 
+    Here, ``unzip_path`` is the directory where you unzip your learnware file, and ``rkme.json`` is your learnware's 
+    statistical specification. 
+
+    .. code-block:: python
+
+        import learnware.specification as specification
+
+        user_spec = specification.rkme.RKMEStatSpecification()
+        user_spec.load(os.path.join(unzip_path, "rkme.json"))
+        user_info = BaseUserInfo(
+            id="user", semantic_spec=user_semantic, stat_info={"RKMEStatSpecification": user_spec}
+        )
+        (sorted_score_list, single_learnware_list,
+            mixture_score, mixture_learnware_list) = easy_market.search_learnware(user_info)
+
 5. Reuse learnwares:
 
+    Based on the returned list of learnwares ``mixture_learnware_list`` in the previous step, 
+    you can easily reuse them to make predictions your own data, instead of training a model from scratch. 
+    We provide two baseline methods for reusing a given list of learnwares, namely ``JobSelectorReuser`` and ``AveragingReuser``.
+
+    .. code-block:: python
+
+        reuse_job_selector = JobSelectorReuser(learnware_list=mixture_learnware_list)
+        job_selector_predict_y = reuse_job_selector.predict(user_data=test_x)
+
+        reuse_ensemble = AveragingReuser(learnware_list=mixture_learnware_list, mode='vote')
+        ensemble_predict_y = reuse_ensemble.predict(user_data=test_x)
+
 .. _script:
 
 Example: Learnware Files